To address these problems, several radio SLAM methods have been proposed. These related studies can be divided into two topics: radio (e.g., 5G) based SLAM and SLAM using the Dirichlet process (DP) method. In the 5G SLAM literature, several techniques based on random finite set (RFS) [1]}, [2]}, [3]}are proposed.
The RFS method has the advantage of dealing with clutter, cardinality of objects that change with time, and data association, but this requires a huge computational cost.
On the other hand, SLAM using message passing (MP) [4]}, [5]}, [6]}, [7]} has a balance between performance and computational complexity, but it is challenging to deal with the number of clutter and unknown objects.
The clustering method-based SLAM techniques for diffuse multipath were also studied in [8]}, [9]}. Among the clustering techniques, DP, which is a non-parametric clustering technique in which the number of clusters is not fixed, is suitable for SLAM scenarios, and various SLAM and tracking techniques using it have been studied: [10]}, [11]}, [12]} considered tracking using DP, while [13]}, [14]}, [15]} introduced a DP SLAM technique that recognizes a landmark by performing data association using a vision sensor or clustering frames and pixels. To the best of our knowledge, DP SLAM has not been considered for radio SLAM applications.
